{"id":"W2060609167","doi":"10.1109/glocomw.2013.6855701","title":"Dynamic access class barring for M2M communications in LTE networks","year":2013,"lang":"en","type":"article","venue":"","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"EnodeB; Computer science; Heuristic; Computer network; Base station; Network packet; User equipment; LTE Advanced; Class (philosophy); Random access; Radio access network; Factor (programming language); Access network; Telecommunications link","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001046364,0.00008958438,0.0001096846,0.00004594748,0.00005337458,0.0001140048,0.0004659272,0.00007795326,0.0001283933],"category_scores_gemma":[0.000004920975,0.00008597951,0.00003398005,0.0001432205,0.00001533616,0.000255907,0.0000995894,0.0001606769,0.00002322104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004416791,"about_ca_system_score_gemma":0.000004992204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007373487,"about_ca_topic_score_gemma":0.0006202263,"domain_scores_codex":[0.9994453,0.00001383511,0.000180403,0.00008257231,0.00003285833,0.0002450571],"domain_scores_gemma":[0.9993815,0.0001210789,0.00001395521,0.0004178129,0.00002303085,0.00004265938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003292778,0.00001625224,0.001927421,0.00006462438,0.00002049178,2.335602e-7,0.00005657368,0.9419148,0.00008121228,0.002573834,0.01061813,0.04272315],"study_design_scores_gemma":[0.0002161552,0.000006796595,0.003286898,0.00003789117,0.000002022245,3.711086e-7,0.00001176192,0.9829391,0.00001749701,0.0009119541,0.01245427,0.0001153169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01646096,0.0009331551,0.8605686,0.001111262,0.0006130679,0.02549729,0.000006280476,0.0009583784,0.09385101],"genre_scores_gemma":[0.9810158,0.00008620189,0.006111351,0.0001359987,0.00004860942,0.01226998,0.00001681025,0.00003185788,0.0002834171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9645548,"threshold_uncertainty_score":0.3506142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02592043185137003,"score_gpt":0.3087231719541647,"score_spread":0.2828027401027947,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}